BACKGROUND: Pulmonary transfusion reactions are important complications of blood transfusion, yet differentiating these clinical syndromes is diagnostically challenging. We hypothesized that biologic markers of inflammation could be used in conjunction with clinical predictors to distinguish transfusion-related acute lung injury (TRALI), transfusion-associated circulatory overload (TACO), and possible TRALI. STUDY DESIGN AND METHODS: In a nested case-control study performed at the University of California at San Francisco and Mayo Clinic, Rochester, we evaluated clinical data and blood samples drawn before and after transfusion in patients with TRALI (n = 70), possible TRALI (n = 48), TACO (n = 29), and controls (n = 147). Cytokines measured included granulocyte-macrophage-colony-stimulating factor, interleukin (IL)-6, IL-8, IL-10, and tumor necrosis factor-α. Logistic regression and receiver operating characteristics curve analyses were used to determine the accuracy of clinical predictors and laboratory markers in differentiating TACO, TRALI, and possible TRALI. RESULTS: Before and after transfusion, IL-6 and IL-8 were elevated in patients with TRALI and possible TRALI relative to controls, and IL-10 was elevated in patients with TACO and possible TRALI relative to that of TRALI and controls. For all pulmonary transfusion reactions, the combination of clinical variables and cytokine measurements displayed optimal diagnostic performance, and the model comparing TACO and TRALI correctly classified 92% of cases relative to expert panel diagnoses. CONCLUSIONS: Before transfusion, there is evidence of systemic inflammation in patients who develop TRALI and possible TRALI but not TACO. A predictive model incorporating readily available clinical and cytokine data effectively differentiated transfusion-related respiratory complications such as TRALI and TACO.
BACKGROUND: Pulmonary transfusion reactions are important complications of blood transfusion, yet differentiating these clinical syndromes is diagnostically challenging. We hypothesized that biologic markers of inflammation could be used in conjunction with clinical predictors to distinguish transfusion-related acute lung injury (TRALI), transfusion-associated circulatory overload (TACO), and possible TRALI. STUDY DESIGN AND METHODS: In a nested case-control study performed at the University of California at San Francisco and Mayo Clinic, Rochester, we evaluated clinical data and blood samples drawn before and after transfusion in patients with TRALI (n = 70), possible TRALI (n = 48), TACO (n = 29), and controls (n = 147). Cytokines measured included granulocyte-macrophage-colony-stimulating factor, interleukin (IL)-6, IL-8, IL-10, and tumor necrosis factor-α. Logistic regression and receiver operating characteristics curve analyses were used to determine the accuracy of clinical predictors and laboratory markers in differentiating TACO, TRALI, and possible TRALI. RESULTS: Before and after transfusion, IL-6 and IL-8 were elevated in patients with TRALI and possible TRALI relative to controls, and IL-10 was elevated in patients with TACO and possible TRALI relative to that of TRALI and controls. For all pulmonary transfusion reactions, the combination of clinical variables and cytokine measurements displayed optimal diagnostic performance, and the model comparing TACO and TRALI correctly classified 92% of cases relative to expert panel diagnoses. CONCLUSIONS: Before transfusion, there is evidence of systemic inflammation in patients who develop TRALI and possible TRALI but not TACO. A predictive model incorporating readily available clinical and cytokine data effectively differentiated transfusion-related respiratory complications such as TRALI and TACO.
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